Most people think of ChatGPT’s Saved Memory as a passive note-taking feature — a static knowledge store the model can “remember” between sessions. I’ve been experimenting with re-tooling this into something more like an active runtime environment — where the memory entry itself contains procedural rules that the model follows automatically, every single exchange, without me re-prompting. For example, I’ve configured it to run a live, persistent token counter that updates after every reply, tracks cumulative totals, calculates cost and energy usage, and always displays in a locked format. It starts at a fixed baseline, deducts usage on each turn, and persists across the entire chat session without breaking the sequence. This effectively transforms memory from a static data vault into a stateful computation layer that lives inside the conversation engine — no API hooks, no extensions, no servers, no scripts. It’s all done internally, purely through memory instructions and careful prompt engineering. This opens up a lot of possibilities: • Internal analytics dashboards that update every turn. • Multi-step, persistent workflows that don’t require manual restating. • Embedded “agents” that survive and adapt across exchanges. It’s a small but fundamental shift — making ChatGPT’s memory do something, not just remember something. Has anyone else played with this idea? I’d be curious about the broader implications for using memory as an in-model automation layer.